This work addresses the collaborative multi-robot autonomous online exploration problem, particularly focusing on distributed exploration planning for dynamically balanced exploration area partition and task allocation among a team of mobile robots operating in obstacle-dense non-convex environments. We present a novel topological map structure that simultaneously characterizes both spatial connectivity and global exploration completeness of the environment. The topological map is updated incrementally to utilize known spatial information for updating reachable spaces, while exploration targets are planned in a receding horizon fashion under global coverage guidance. A distributed weighted topological graph Voronoi algorithm is introduced implementing balanced graph space partitions of the fused topological maps. Theoretical guarantees are provided for distributed consensus convergence and equitable graph space partitions with constant bounds. A local planner optimizes the visitation sequence of exploration targets within the balanced partitioned graph space to minimize travel distance, while generating safe, smooth, and dynamically feasible motion trajectories. Comprehensive benchmarking against state-of-the-art methods demonstrates significant improvements in exploration efficiency, completeness, and workload balance across the robot team.

翻译：本研究针对协作多机器人自主在线探索问题，重点研究在障碍物密集的非凸环境中，移动机器人团队实现动态平衡探索区域划分与任务分配的分布式探索规划。我们提出了一种新颖的拓扑地图结构，可同时表征环境的空间连通性与全局探索完备性。该拓扑地图通过增量更新机制利用已知空间信息更新可达区域，同时在全局覆盖引导下以滚动时域方式规划探索目标。本文引入分布式加权拓扑图Voronoi算法，实现对融合拓扑图的平衡图空间划分。研究从理论上证明了分布式共识收敛性及具有恒定边界的公平图空间划分。局部规划器在平衡划分的图空间内优化探索目标的访问序列以最小化行进距离，同时生成安全、平滑且动态可行的运动轨迹。与前沿方法的全面对比实验表明，该方法在机器人团队的探索效率、完备性和工作负载均衡方面均有显著提升。